كنترل پوشش توزيع‌شده سيستم چند عامله در يك منطقه بحران‌زده و مكانيابي اهداف ساكن احتمالي موجود در منطقه

Distributed Coverage Control of a Multi Agent System in a Disaster Area and Localizing the Stationary Probable Targets in the Environment

اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﻣﺴﺎﻟﻪ ﺟﺴﺘﺠﻮ و ﭘﻮﺷﺶ ﻣﺸﺎرﮐﺘﯽ اﺧﺘﺼﺎص دارد ﮐﻪ در آن ﯾﮏ اﻟﮕﻮرﯾﺘﻢ ﮐﻨﺘﺮل ﺗﻮزﯾﻊ ﺷﺪه ﺑﺮاي ﺟﺴﺘﺠﻮي ﺧﻮدﮐﺎر ﻗﺮﺑﺎﻧﯿﺎن ﺑﻼﯾﺎي ﻃﺒﯿﻌﯽ ﺑﺎ اﺳﺘﻔﺎده از ﺳﯿﺴﺘﻢ ﭼﻨﺪ ﻋﺎﻣﻠﻪ ﻣﺸﺎرﮐﺘﯽ ﺑﻪ ﮐﺎر ﮔﺮﻓﺘﻪ ﺷﺪه اﺳﺖ. ﻣﺪﯾﺮﯾﺖ ﺑﺤﺮان ﯾﮏ ﻣﺴﺎﺑﻘﻪ ﺑﺎ زﻣﺎن اﺳﺖ. ﺑﻨﺎﺑﺮاﯾﻦ ﭘﺎﺳﺦ ﺳﺮﯾﻊ ﺑﻪ ﺑﺤﺮان ﻣﯽﺗﻮاﻧﺪ ﺑﻪ ﻃﻮر ﭼﺸﻤﮕﯿﺮي از ﻣﯿﺰان ﺧﺴﺎرات و آﺳﯿﺐﻫﺎ ﺑﮑﺎﻫﺪ. ﺑﺪﯾﻬﯽ اﺳﺖ ﮐﻪ ﯾﮏ ﻧﻘﺸﻪ ﻓﺮاﮔﯿﺮ از ﻣﻨﻄﻘﻪ ﺑﺤﺮانزد ﺑﻼﻓﺎﺻﻠﻪ ﭘﺲ از وﻗﻮع ﺑﻪ ﺗﯿﻢ ﮐﻨﺘﺮل ﺑﺤﺮان ﮐﻤﮏ ﻣﯽﮐﻨﺪ ﺗﺎ ﻋﻤﻠﯿﺎت ﻣﺪﯾﺮﯾﺖ ﺑﺤﺮان را ﺑﺎ اﻃﻼﻋﺎت ﺑﯿﺸﺘﺮ و دﻗﯿﻖﺗﺮي ﻃﺮاﺣﯽ ﻧﻤﺎﯾﺪ. در اﯾﻦ ﺗﺤﻘﯿﻖ ﯾﮏ ﻣﺤﯿﻂ ﻣﺴﺘﻄﯿﻠﯽ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪه ﮐﻪ 4 ﭘﺮﻧﺪه ﺑﺎل-ﺛﺎﺑﺖ ﺑﺎ ﭘﺮواز ﺑﺮ ﻓﺮاز آن ﻋﻼوه ﺑﺮ ﭘﻮﺷﺶ ﺣﺪاﮐﺜﺮي ﻣﺤﯿﻂ، ﻣﺤﻞ دﻗﯿﻖ 18 ﻫﺪف ﺳﺎﮐﻦ ﺗﻮزﯾﻊﺷﺪه را ﻣﯽﯾﺎﺑﻨﺪ. ﺑﺪﯾﻦ ﻣﻨﻈﻮر، UAVﻫﺎ ﺑﺎ ﻫﻤﮑﺎري ﯾﮑﺪﯾﮕﺮ ﻧﻘﺸﻪ ﺷﻨﺎﺧﺖ ﺷﺎﻣﻞ ﻧﻘﺸﻪ ﻫﺎي اﺣﺘﻤﺎل ﺣﻀﻮر ﻫﺪف ﻧﺎﻣﻌﯿﻨﯽ را ﻣﯽﺳﺎزﻧﺪ و ﺑﺮاﺳﺎس ﻫﻤﯿﻦ ﻧﻘﺸﻪ ﺷﻨﺎﺧﺖ و ﻫﻤﭽﻨﯿﻦ ﭘﯿﺶﺑﯿﻨﯽ ﺳﻪ ﺣﺮﮐﺖ ﺑﻌﺪي ﺧﻮد و ﻋﺎﻣﻞﻫﺎي ﻫﻤﺴﺎﯾﻪ، ﺑﻬﺘﺮﯾﻦ ﻣﺴﯿﺮ ﺑﺪ ن ﺑﺮﺧﻮرد را ﻣﺤﺎﺳﺒﻪ ﻣﯽﮐﻨﻨﺪ. ﻣﻘﺎﯾﺴﻪ روش ﻣﺸﺎرﮐﺘﯽ و ﻏﯿﺮ ﻣﺸﺎرﮐﺘﯽ ﻧﺸﺎن ﻣﯽدﻫﺪ، در روش ﮐﻨﺘﺮل ﺗﻮزﯾﻊ ﺷﺪه ﻣﺸﺎرﮐﺘﯽ، درﺻﺪ ﭘﻮﺷﺶ ﻣﺤﯿﻂ و ﻣﯿﺎﻧﮕﯿﻦ ﻧﺎﻣﻌﯿﻨﯽ ﺑﺴﯿﺎر زودﺗﺮ ﺑﻪ ﺣﺪاﮐﺜﺮ و ﺣﺪاﻗﻞ ﻣﻘﺪار ﻗﺎﺑﻞ ﻗﺒﻮل ﺧﻮد ﻣﯽرﺳﺪ.

This paper is dedicated to a cooperative search and coverage problem in which we applied a distributed coverage control algorithm for automated victim search using a cooperative multi-UAVs system. Disaster management is a competition with time. So, fast response to disaster can significantly decrease damages and losses. It is obvious that a comprehensive map of the affected area immediately after disaster occurred, helps disaster control unit to quickly and initially evaluate the degree of damages, amount of economic losses and casualties. Therefore, they can plan for disaster management operations with more and detailed information. We considered a rectangular surveillance region in which four fixed-wing UAVs fly over the affected area to exactly localize the 18 stationary targets randomly distributed in the environment, as well as maximizing the coverage. For this purpose, UAVs cooperatively build the cognitive maps including Target Probability Map and Uncertainty Map. Then based on cognitive map and also their prediction from three steps ahead of their own and also neighboring agents moves, UAVs decide about their optimal collision-free paths. Comparison between cooperative and non-cooperative methods indicates that in cooperative distributed scheme, coverage percentage and global average uncertainty converges to their admissible maximum and minimum value in a considerably less time, respectively.